Optimal Charging

Optimal charging strategies for electric vehicles (EVs) aim to minimize charging costs and maximize grid stability by intelligently managing charging schedules and locations. Current research heavily utilizes reinforcement learning, particularly deep reinforcement learning (DRL) algorithms like DDPG and variations of federated learning, to develop adaptive charging policies that account for dynamic factors such as real-time pricing, solar energy availability, and network congestion. These approaches address challenges like peak demand, driver preferences, and privacy concerns, offering significant potential for improving grid efficiency and reducing the environmental impact of EV adoption. The development of robust and scalable algorithms is crucial for widespread implementation and integration into smart grid infrastructure.